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Merge feat/freqai into develop to get new features

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.gitignore vendored
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user_data/*
!user_data/strategy/sample_strategy.py
!user_data/notebooks
!user_data/models
!user_data/freqaimodels
user_data/freqaimodels/*
user_data/models/*
user_data/notebooks/*
freqtrade-plot.html
freqtrade-profit-plot.html
@ -105,3 +109,5 @@ target/
!config_examples/config_ftx.example.json
!config_examples/config_full.example.json
!config_examples/config_kraken.example.json
!config_examples/config_freqai_futures.example.json
!config_examples/config_freqai_spot.example.json

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{
"trading_mode": "futures",
"margin_mode": "isolated",
"max_open_trades": 5,
"stake_currency": "USDT",
"stake_amount": 200,
"tradable_balance_ratio": 1,
"fiat_display_currency": "USD",
"dry_run": true,
"timeframe": "3m",
"dry_run_wallet": 1000,
"cancel_open_orders_on_exit": true,
"unfilledtimeout": {
"entry": 10,
"exit": 30
},
"exchange": {
"name": "okx",
"key": "",
"secret": "",
"ccxt_config": {
"enableRateLimit": true
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
"AGLD/USDT:USDT",
"1INCH/USDT:USDT",
"AAVE/USDT:USDT",
"ALGO/USDT:USDT",
"ALPHA/USDT:USDT",
"API3/USDT:USDT",
"AVAX/USDT:USDT",
"AXS/USDT:USDT",
"BCH/USDT:USDT"
],
"pair_blacklist": []
},
"entry_pricing": {
"price_side": "same",
"use_order_book": true,
"order_book_top": 1,
"price_last_balance": 0.0,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"exit_pricing": {
"price_side": "other",
"use_order_book": true,
"order_book_top": 1
},
"pairlists": [
{
"method": "StaticPairList"
}
],
"freqai": {
"startup_candles": 10000,
"timeframes": [
"3m",
"15m",
"1h"
],
"train_period": 20,
"backtest_period": 0.001,
"identifier": "constant_retrain_live",
"live_trained_timestamp": 0,
"corr_pairlist": [
"BTC/USDT:USDT",
"ETH/USDT:USDT"
],
"feature_parameters": {
"period": 20,
"shift": 2,
"DI_threshold": 0.9,
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": true,
"stratify": 0,
"indicator_max_period": 20,
"indicator_periods": [10, 20]
},
"data_split_parameters": {
"test_size": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 1000,
"task_type": "CPU"
}
},
"bot_name": "",
"force_entry_enable": true,
"initial_state": "running",
"internals": {
"process_throttle_secs": 5
}
}

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{
"max_open_trades": 1,
"stake_currency": "USDT",
"stake_amount": 900,
"tradable_balance_ratio": 1,
"fiat_display_currency": "USD",
"dry_run": true,
"timeframe": "5m",
"dry_run_wallet": 4000,
"dataformat_ohlcv": "json",
"cancel_open_orders_on_exit": true,
"unfilledtimeout": {
"entry": 10,
"exit": 30
},
"exchange": {
"name": "binance",
"key": "",
"secret": "",
"ccxt_config": {
"enableRateLimit": true
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
"BTC/USDT",
"ETH/USDT"
],
"pair_blacklist": []
},
"entry_pricing": {
"price_side": "same",
"use_order_book": true,
"order_book_top": 1,
"price_last_balance": 0.0,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"exit_pricing": {
"price_side": "other",
"use_order_book": true,
"order_book_top": 1
},
"pairlists": [
{
"method": "StaticPairList"
}
],
"freqai": {
"startup_candles": 10000,
"timeframes": [
"5m",
"15m",
"4h"
],
"train_period": 30,
"backtest_period": 7,
"identifier": "example",
"live_trained_timestamp": 0,
"corr_pairlist": [
"BTC/USDT",
"ETH/USDT",
"DOT/USDT",
"MATIC/USDT",
"SOL/USDT"
],
"feature_parameters": {
"period": 500,
"shift": 1,
"DI_threshold": 0,
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": false,
"stratify": 0,
"indicator_max_period": 50,
"indicator_periods": [10, 20]
},
"data_split_parameters": {
"test_size": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 1000,
"task_type": "CPU"
}
},
"bot_name": "",
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
}
}

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ARG sourceimage=freqtradeorg/freqtrade
ARG sourcetag=develop
FROM ${sourceimage}:${sourcetag}
USER root
RUN apt-get install -y libgomp1
USER ftuser
# Install dependencies
COPY requirements-freqai.txt /freqtrade/
RUN pip install -r requirements-freqai.txt --user --no-cache-dir
# Temporary step - as the source image will contain the wrong (non-freqai) sourcecode
COPY --chown=ftuser:ftuser . /freqtrade/

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# Freqai
!!! Note
Freqai is still experimental, and should be used at the user's own discretion.
Freqai is a module designed to automate a variety of tasks associated with
training a predictive model to provide signals based on input features.
Among the the features included:
* Easy large feature set construction based on simple user input
* Sweep model training and backtesting to simulate consistent model retraining through time
* Smart outlier removal of data points from prediction sets using a Dissimilarity Index.
* Data dimensionality reduction with Principal Component Analysis
* Automatic file management for storage of models to be reused during live
* Smart and safe data standardization
* Cleaning of NaNs from the data set before training and prediction.
* Automated live retraining (still VERY experimental. Proceed with caution.)
## General approach
The user provides FreqAI with a set of custom indicators (created inside the strategy the same way
a typical Freqtrade strategy is created) as well as a target value (typically some price change into
the future). FreqAI trains a model to predict the target value based on the input of custom indicators.
FreqAI will train and save a new model for each pair in the config whitelist.
Users employ FreqAI to backtest a strategy (emulate reality with retraining a model as new data is
introduced) and run the model live to generate buy and sell signals.
## Background and vocabulary
**Features** are the quantities with which a model is trained. $X_i$ represents the
vector of all features for a single candle. In Freqai, the user
builds the features from anything they can construct in the strategy.
**Labels** are the target values with which the weights inside a model are trained
toward. Each set of features is associated with a single label, which is also
defined within the strategy by the user. These labels look forward into the
future, and are not available to the model during dryrun/live/backtesting.
**Training** refers to the process of feeding individual feature sets into the
model with associated labels with the goal of matching input feature sets to
associated labels.
**Train data** is a subset of the historic data which is fed to the model during
training to adjust weights. This data directly influences weight connections
in the model.
**Test data** is a subset of the historic data which is used to evaluate the
intermediate performance of the model during training. This data does not
directly influence nodal weights within the model.
## Install prerequisites
Use `pip` to install the prerequisites with:
`pip install -r requirements-freqai.txt`
## Running from the example files
An example strategy, an example prediction model, and example config can all be found in
`freqtrade/templates/ExampleFreqaiStrategy.py`,
`freqtrade/freqai/prediction_models/CatboostPredictionModel.py`,
`config_examples/config_freqai.example.json`, respectively. Assuming the user has downloaded
the necessary data, Freqai can be executed from these templates with:
```bash
freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostPredictionModel --strategy-path freqtrade/templates --timerange 20220101-20220201
```
## Configuring the bot
### Example config file
The user interface is isolated to the typical config file. A typical Freqai
config setup includes:
```json
"freqai": {
"startup_candles": 10000,
"timeframes" : ["5m","15m","4h"],
"train_period" : 30,
"backtest_period" : 7,
"identifier" : "unique-id",
"corr_pairlist": [
"ETH/USD",
"LINK/USD",
"BNB/USD"
],
"feature_parameters" : {
"period": 24,
"shift": 2,
"weight_factor": 0,
},
"data_split_parameters" : {
"test_size": 0.25,
"random_state": 42
},
"model_training_parameters" : {
"n_estimators": 100,
"random_state": 42,
"learning_rate": 0.02,
"task_type": "CPU",
},
}
```
### Building the feature set
!! slightly out of date, please refer to templates/FreqaiExampleStrategy.py for updated method !!
Features are added by the user inside the `populate_any_indicators()` method of the strategy
by prepending indicators with `%`:
```python
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
informative['%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14)
informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
informative['%-' + coin + "adx"] = ta.ADX(informative, window=20)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
informative[coin + "bb_lowerband"] = bollinger["lower"]
informative[coin + "bb_middleband"] = bollinger["mid"]
informative[coin + "bb_upperband"] = bollinger["upper"]
informative['%-' + coin + "bb_width"] = (
informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
) / informative[coin + "bb_middleband"]
# The following code automatically adds features according to the `shift` parameter passed
# in the config. Do not remove
indicators = [col for col in informative if col.startswith('%')]
for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
# The following code safely merges into the base timeframe.
# Do not remove.
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]]
df = df.drop(columns=skip_columns)
```
The user of the present example does not want to pass the `bb_lowerband` as a feature to the model,
and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
model for training/prediction and has therfore prepended it with `%`._
Note: features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()`
will fail in live/dry. If the user wishes to add generalized features that are not associated with
a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`:
```python
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
# Add generalized indicators here (because in live, it will call only this function to populate
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
# these generalized indicators to the basepair/timeframe
if pair == metadata['pair'] and tf == self.timeframe:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
```
(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`)
The `timeframes` from the example config above are the timeframes of each `populate_any_indicator()`
included metric for inclusion in the feature set. In the present case, the user is asking for the
`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included
in the feature set.
In addition, the user can ask for each of these features to be included from
informative pairs using the `corr_pairlist`. This means that the present feature
set will include all the `base_features` on all the `timeframes` for each of
`ETH/USD`, `LINK/USD`, and `BNB/USD`.
`shift` is another user controlled parameter which indicates the number of previous
candles to include in the present feature set. In other words, `shift: 2`, tells
Freqai to include the the past 2 candles for each of the features included
in the dataset.
In total, the number of features the present user has created is:_
no. `timeframes` * no. `base_features` * no. `corr_pairlist` * no. `shift`_
3 * 3 * 3 * 2 = 54._
### Deciding the sliding training window and backtesting duration
Users define the backtesting timerange with the typical `--timerange` parameter in the user
configuration file. `train_period` is the duration of the sliding training window, while
`backtest_period` is the sliding backtesting window, both in number of days (backtest_period can be
a float to indicate sub daily retraining in live/dry mode). In the present example,
the user is asking Freqai to use a training period of 30 days and backtest the subsequent 7 days.
This means that if the user sets `--timerange 20210501-20210701`,
Freqai will train 8 separate models (because the full range comprises 8 weeks),
and then backtest the subsequent week associated with each of the 8 training
data set timerange months. Users can think of this as a "sliding window" which
emulates Freqai retraining itself once per week in live using the previous
month of data._
In live, the required training data is automatically computed and downloaded. However, in backtesting
the user must manually enter the required number of `startup_candles` in the config. This value
is used to increase the available data to FreqAI and should be sufficient to enable all indicators
to be NaN free at the beginning of the first training timerange. This boils down to identifying the
highest timeframe (`4h` in present example) and the longest indicator period (25 in present example)
and adding this to the `train_period`. The units need to be in the base candle time frame:_
`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period * 1440 minutes per day ) / 5 min (base time frame) = 1488.
!!! Note: in dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no
influence on dry/live.
## Running Freqai
### Training and backtesting
The freqai training/backtesting module can be executed with the following command:
```bash
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel CatboostPredictionModel --timerange 20210501-20210701
```
If this command has never been executed with the existing config file, then it will train a new model
for each pair, for each backtesting window within the bigger `--timerange`._
---
**NOTE**
Once the training is completed, the user can execute this again with the same config file and
FreqAI will find the trained models and load them instead of spending time training. This is useful
if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user
*wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`.
This way, the user can return to using any model they wish by simply changing the `identifier`.
---
### Building a freqai strategy
The Freqai strategy requires the user to include the following lines of code in the strategy:
```python
from freqtrade.freqai.strategy_bridge import CustomModel
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config['freqai']
# the following loops are necessary for building the features
# indicated by the user in the configuration file.
for tf in self.freqai_info['timeframes']:
for i in self.freqai_info['corr_pairlist']:
dataframe = self.populate_any_indicators(i,
dataframe.copy(), tf, coin=i.split("/")[0]+'-')
# the model will return 4 values, its prediction, an indication of whether or not the prediction
# should be accepted, the target mean/std values from the labels used during each training period.
(dataframe['prediction'], dataframe['do_predict'],
dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata)
return dataframe
```
The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds
the feature set with a proper naming convention for the IFreqaiModel to use later.
### Building an IFreqaiModel
Freqai has an example prediction model based on the popular `Catboost` regression (`freqai/prediction_models/CatboostPredictionModel.py`). However, users can customize and create
their own prediction models using the `IFreqaiModel` class. Users are encouraged to inherit `train()`, `predict()`,
and `make_labels()` to let them customize various aspects of their training procedures.
### Running the model live
Freqai can be run dry/live using the following command
```bash
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel
```
By default, Freqai will not find find any existing models and will start by training a new one
given the user configuration settings. Following training, it will use that model to predict for the
duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain
a new model, and begin making predictions with the updated model. FreqAI backtesting and live both
permit the user to use fractional days (i.e. 0.1) in the `backtest_period`, which enables more frequent
retraining. But the user should be careful that using a fractional `backtest_period` with a large
`--timerange` in backtesting will result in a huge amount of required trainings/models.
If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
the same `identifier` parameter
```json
"freqai": {
"identifier": "example",
}
```
In this case, although Freqai will initiate with a
pre-trained model, it will still check to see how much time has elapsed since the model was trained,
and if a full `backtest_period` has elapsed since the end of the loaded model, FreqAI will self retrain.
## Data anylsis techniques
### Controlling the model learning process
The user can define model settings for the data split `data_split_parameters` and learning parameters
`model_training_parameters`. Users are encouraged to visit the Catboost documentation
for more information on how to select these values. `n_estimators` increases the
computational effort and the fit to the training data. If a user has a GPU
installed in their system, they may benefit from changing `task_type` to `GPU`.
The `weight_factor` allows the user to weight more recent data more strongly
than past data via an exponential function:
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._
![weight-factor](assets/weights_factor.png)
Finally, `period` defines the offset used for the `labels`. In the present example,
the user is asking for `labels` that are 24 candles in the future.
### Removing outliers with the Dissimilarity Index
The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each
prediction by the model. To do so, Freqai measures the distance between each training
data point and all other training data points:
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$
is the number of features i.e. the length of the vector $X$. The
characteristic distance, $\overline{d}$ for a set of training data points is simply the mean
of the average distances:
$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
$\overline{d}$ quantifies the spread of the training data, which is compared to
the distance between the new prediction feature vectors, $X_k$ and all the training
data:
$$ d_k = \argmin_i d_{k,i} $$
which enables the estimation of a Dissimilarity Index:
$$ DI_k = d_k/\overline{d} $$
Equity and crypto markets suffer from a high level of non-patterned noise in the
form of outlier data points. The dissimilarity index allows predictions which
are outliers and not existent in the model feature space, to be thrown out due
to low levels of certainty. Activating the Dissimilarity Index can be achieved with:
```json
"freqai": {
"feature_parameters" : {
"DI_threshold": 1
}
}
```
The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the
trained model.
### Reducing data dimensionality with Principal Component Analysis
Users can reduce the dimensionality of their features by activating the `principal_component_analysis`:
```json
"freqai": {
"feature_parameters" : {
"principal_component_analysis": true
}
}
```
Which will perform PCA on the features and reduce the dimensionality of the data so that the explained
variance of the data set is >= 0.999.
### Removing outliers using a Support Vector Machine (SVM)
The user can tell Freqai to remove outlier data points from the training/test data sets by setting:
```json
"freqai": {
"feature_parameters" : {
"use_SVM_to_remove_outliers: true
}
}
```
Freqai will train an SVM on the training data (or components if the user activated
`principal_component_analysis`) and remove any data point that it deems to be sit beyond the
feature space.
### Stratifying the data
The user can stratify the training/testing data using:
```json
"freqai": {
"feature_parameters" : {
"stratify": 3
}
}
```
which will split the data chronologically so that every Xth data points is a testing data point. In the
present example, the user is asking for every third data point in the dataframe to be used for
testing, the other points are used for training.
### Setting up a follower
The user can define:
```json
"freqai": {
"follow_mode": true,
"identifier": "example"
}
```
to indicate to the bot that it should not train models, but instead should look for models trained
by a leader with the same `identifier`. In this example, the user has a leader bot with the
`identifier: "example"` already running or launching simultaneously as the present follower.
The follower will load models created by the leader and inference them to obtain predictions.
### Purging old model data
FreqAI stores new model files each time it retrains. These files become obsolete as new models
are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running
for extended periods of time with high frequency retraining should set `purge_old_models` in their
config:
```json
"freqai": {
"purge_old_models": true,
}
```
which will automatically purge all models older than the two most recently trained ones.
## Defining model expirations
During dry/live, FreqAI trains each pair sequentially (on separate threads/GPU from the main
Freqtrade bot). This means there is always an age discrepancy between models. If a user is training
on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old.
This may be undesirable if the characteristic time scale (read trade duration target) for a strategy
is much less than 4 hours. The user can decide to only make trade entries if the model is less than
a certain number of hours in age by setting the `expiration_hours` in the config file:
```json
"freqai": {
"expiration_hours": 0.5,
}
```
In the present example, the user will only allow predictions on models that are less than 1/2 hours
old.
<!-- ## Dynamic target expectation
The labels used for model training have a unique statistical distribution for each separate model training.
We can use this information to know if our current prediction is in the realm of what the model was trained on,
and if so, what is the statistical probability of the current prediction. With this information, we can
make more informed prediction._
FreqAI builds this label distribution and provides a quantile to the strategy, which can be optionally used as a
dynamic threshold. The `target_quantile: X` means that X% of the labels are below this value. So setting:
```json
"freqai": {
"feature_parameters" : {
"target_quantile": 0.9
}
}
```
Means the user will get back in the strategy the label threshold at which 90% of the labels were
below this value. An example usage in the strategy may look something like:
```python
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# ... #
(
dataframe["prediction"],
dataframe["do_predict"],
dataframe["target_upper_quantile"],
dataframe["target_lower_quantile"],
) = self.model.bridge.start(dataframe, metadata, self)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
buy_conditions = [
(dataframe["prediction"] > dataframe["target_upper_quantile"]) & (dataframe["do_predict"] == 1)
]
if buy_conditions:
dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1
return dataframe
``` -->
## Additional information
### Feature normalization
The feature set created by the user is automatically normalized to the training
data only. This includes all test data and unseen prediction data (dry/live/backtest).
### File structure
`user_data_dir/models/` contains all the data associated with the trainings and
backtests. This file structure is heavily controlled and read by the `FreqaiDataKitchen()`
and should thus not be modified.

3
freqtrade/commands/arguments.py
View File

@ -12,7 +12,8 @@ from freqtrade.constants import DEFAULT_CONFIG
ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search"]
ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search", "freqaimodel",
"freqaimodel_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]

10
freqtrade/commands/cli_options.py
View File

@ -647,4 +647,14 @@ AVAILABLE_CLI_OPTIONS = {
nargs='+',
default=[],
),
"freqaimodel": Arg(
'--freqaimodel',
help='Specify a custom freqaimodels.',
metavar='NAME',
),
"freqaimodel_path": Arg(
'--freqaimodel-path',
help='Specify additional lookup path for freqaimodels.',
metavar='PATH',
),
}

5
freqtrade/commands/data_commands.py
View File

@ -12,7 +12,7 @@ from freqtrade.enums import CandleType, RunMode, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.exchange.exchange import market_is_active
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
from freqtrade.resolvers import ExchangeResolver
@ -50,7 +50,8 @@ def start_download_data(args: Dict[str, Any]) -> None:
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
markets = [p for p, m in exchange.markets.items() if market_is_active(m)
or config.get('include_inactive')]
expanded_pairs = expand_pairlist(config['pairs'], markets)
expanded_pairs = dynamic_expand_pairlist(config, markets)
# Manual validations of relevant settings
if not config['exchange'].get('skip_pair_validation', False):

16
freqtrade/configuration/config_validation.py
View File

@ -85,6 +85,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_unlimited_amount(conf)
_validate_ask_orderbook(conf)
validate_migrated_strategy_settings(conf)
_validate_freqai(conf)
# validate configuration before returning
logger.info('Validating configuration ...')
@ -163,6 +164,21 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
)
def _validate_freqai(conf: Dict[str, Any]) -> None:
"""
Freqai param validator
"""
if not conf.get('freqai', {}):
return
for param in constants.SCHEMA_FREQAI_REQUIRED:
if param not in conf.get('freqai', {}):
raise OperationalException(
f'{param} not found in Freqai config'
)
def _validate_whitelist(conf: Dict[str, Any]) -> None:
"""
Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does.

12
freqtrade/configuration/configuration.py
View File

@ -97,6 +97,8 @@ class Configuration:
self._process_analyze_options(config)
self._process_freqai_options(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
@ -461,6 +463,16 @@ class Configuration:
config.update({'runmode': self.runmode})
def _process_freqai_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='freqaimodel',
logstring='Using freqaimodel class name: {}')
self._args_to_config(config, argname='freqaimodel_path',
logstring='Using freqaimodel path: {}')
return
def _args_to_config(self, config: Dict[str, Any], argname: str,
logstring: str, logfun: Optional[Callable] = None,
deprecated_msg: Optional[str] = None) -> None:

51
freqtrade/constants.py
View File

@ -55,6 +55,7 @@ FTHYPT_FILEVERSION = 'fthypt_fileversion'
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
USERPATH_FREQAIMODELS = 'freqaimodels'
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
@ -472,7 +473,44 @@ CONF_SCHEMA = {
'remove_pumps': {'type': 'boolean'}
},
'required': ['process_throttle_secs', 'allowed_risk']
}
},
"freqai": {
"type": "object",
"properties": {
"timeframes": {"type": "list"},
"train_period": {"type": "integer", "default": 0},
"backtest_period": {"type": "float", "default": 7},
"identifier": {"type": "str", "default": "example"},
"corr_pairlist": {"type": "list"},
"feature_parameters": {
"type": "object",
"properties": {
"period": {"type": "integer"},
"shift": {"type": "integer", "default": 0},
"DI_threshold": {"type": "float", "default": 0},
"weight_factor": {"type": "number", "default": 0},
"principal_component_analysis": {"type": "boolean", "default": False},
"use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
},
},
"data_split_parameters": {
"type": "object",
"properties": {
"test_size": {"type": "number"},
"random_state": {"type": "integer"},
},
},
"model_training_parameters": {
"type": "object",
"properties": {
"n_estimators": {"type": "integer", "default": 2000},
"random_state": {"type": "integer", "default": 1},
"learning_rate": {"type": "number", "default": 0.02},
"task_type": {"type": "string", "default": "CPU"},
},
},
},
},
},
}
@ -516,6 +554,17 @@ SCHEMA_MINIMAL_REQUIRED = [
'dataformat_trades',
]
SCHEMA_FREQAI_REQUIRED = [
'timeframes',
'train_period',
'backtest_period',
'identifier',
'corr_pairlist',
'feature_parameters',
'data_split_parameters',
'model_training_parameters'
]
CANCEL_REASON = {
"TIMEOUT": "cancelled due to timeout",
"PARTIALLY_FILLED_KEEP_OPEN": "partially filled - keeping order open",

4
freqtrade/exchange/exchange.py
View File

@ -86,7 +86,7 @@ class Exchange:
# TradingMode.SPOT always supported and not required in this list
]
def __init__(self, config: Dict[str, Any], validate: bool = True) -> None:
def __init__(self, config: Dict[str, Any], validate: bool = True, freqai: bool = False) -> None:
"""
Initializes this module with the given config,
it does basic validation whether the specified exchange and pairs are valid.
@ -196,7 +196,7 @@ class Exchange:
self.markets_refresh_interval: int = exchange_config.get(
"markets_refresh_interval", 60) * 60
if self.trading_mode != TradingMode.SPOT:
if self.trading_mode != TradingMode.SPOT and freqai is False:
self.fill_leverage_tiers()
self.additional_exchange_init()

314
freqtrade/freqai/data_drawer.py Normal file
View File

@ -0,0 +1,314 @@
import collections
import json
import logging
import re
import shutil
import threading
from pathlib import Path
from typing import Any, Dict, Tuple
# import pickle as pk
import numpy as np
import pandas as pd
from pandas import DataFrame
# from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class FreqaiDataDrawer:
"""
Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
/loading to/from disk.
This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is
reinstantiated for each coin.
"""
def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
self.config = config
self.freqai_info = config.get("freqai", {})
# dictionary holding all pair metadata necessary to load in from disk
self.pair_dict: Dict[str, Any] = {}
# dictionary holding all actively inferenced models in memory given a model filename
self.model_dictionary: Dict[str, Any] = {}
self.model_return_values: Dict[str, Any] = {}
self.pair_data_dict: Dict[str, Any] = {}
self.historic_data: Dict[str, Any] = {}
self.follower_dict: Dict[str, Any] = {}
self.full_path = full_path
self.follow_mode = follow_mode
if follow_mode:
self.create_follower_dict()
self.load_drawer_from_disk()
self.training_queue: Dict[str, int] = {}
self.history_lock = threading.Lock()
def load_drawer_from_disk(self):
"""
Locate and load a previously saved data drawer full of all pair model metadata in
present model folder.
:returns:
exists: bool = whether or not the drawer was located
"""
exists = Path(self.full_path / str("pair_dictionary.json")).resolve().exists()
if exists:
with open(self.full_path / str("pair_dictionary.json"), "r") as fp:
self.pair_dict = json.load(fp)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
else:
logger.warning(
f"Follower could not find pair_dictionary at {self.full_path} "
"sending null values back to strategy"
)
return exists
def save_drawer_to_disk(self):
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with open(self.full_path / str("pair_dictionary.json"), "w") as fp:
json.dump(self.pair_dict, fp, default=self.np_encoder)
def save_follower_dict_to_disk(self):
"""
Save follower dictionary to disk (used by strategy for persistent prediction targets)
"""
follower_name = self.config.get("bot_name", "follower1")
with open(
self.full_path / str("follower_dictionary-" + follower_name + ".json"), "w"
) as fp:
json.dump(self.follower_dict, fp, default=self.np_encoder)
def create_follower_dict(self):
"""
Create or dictionary for each follower to maintain unique persistent prediction targets
"""
follower_name = self.config.get("bot_name", "follower1")
whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
exists = (
Path(self.full_path / str("follower_dictionary-" + follower_name + ".json"))
.resolve()
.exists()
)
if exists:
logger.info("Found an existing follower dictionary")
for pair in whitelist_pairs:
self.follower_dict[pair] = {}
with open(
self.full_path / str("follower_dictionary-" + follower_name + ".json"), "w"
) as fp:
json.dump(self.follower_dict, fp, default=self.np_encoder)
def np_encoder(self, object):
if isinstance(object, np.generic):
return object.item()
def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool, bool]:
"""
Locate and load existing model metadata from persistent storage. If not located,
create a new one and append the current pair to it and prepare it for its first
training
:params:
metadata: dict = strategy furnished pair metadata
:returns:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
coin_first: bool = If the coin is fresh without metadata
return_null_array: bool = Follower could not find pair metadata
"""
pair_in_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, {}).get("data_path", None)
return_null_array = False
if pair_in_dict:
model_filename = self.pair_dict[pair]["model_filename"]
trained_timestamp = self.pair_dict[pair]["trained_timestamp"]
coin_first = self.pair_dict[pair]["first"]
elif not self.follow_mode:
self.pair_dict[pair] = {}
model_filename = self.pair_dict[pair]["model_filename"] = ""
coin_first = self.pair_dict[pair]["first"] = True
trained_timestamp = self.pair_dict[pair]["trained_timestamp"] = 0
self.pair_dict[pair]["priority"] = len(self.pair_dict)
if not data_path_set and self.follow_mode:
logger.warning(
f"Follower could not find current pair {pair} in "
f"pair_dictionary at path {self.full_path}, sending null values "
"back to strategy."
)
return_null_array = True
return model_filename, trained_timestamp, coin_first, return_null_array
def set_pair_dict_info(self, metadata: dict) -> None:
pair_in_dict = self.pair_dict.get(metadata["pair"])
if pair_in_dict:
return
else:
self.pair_dict[metadata["pair"]] = {}
self.pair_dict[metadata["pair"]]["model_filename"] = ""
self.pair_dict[metadata["pair"]]["first"] = True
self.pair_dict[metadata["pair"]]["trained_timestamp"] = 0
self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
return
def pair_to_end_of_training_queue(self, pair: str) -> None:
# march all pairs up in the queue
for p in self.pair_dict:
self.pair_dict[p]["priority"] -= 1
# send pair to end of queue
self.pair_dict[pair]["priority"] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, dk, pred_df, do_preds) -> None:
"""
Set the initial return values to a persistent dataframe. This avoids needing to repredict on
historical candles, and also stores historical predictions despite retrainings (so stored
predictions are true predictions, not just inferencing on trained data)
"""
self.model_return_values[pair] = pd.DataFrame()
for label in dk.label_list:
self.model_return_values[pair][label] = pred_df[label]
self.model_return_values[pair][f"{label}_mean"] = dk.data["labels_mean"][label]
self.model_return_values[pair][f"{label}_std"] = dk.data["labels_std"][label]
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
self.model_return_values[pair]["DI_values"] = dk.DI_values
self.model_return_values[pair]["do_predict"] = do_preds
def append_model_predictions(self, pair: str, predictions, do_preds, dk, len_df) -> None:
# strat seems to feed us variable sized dataframes - and since we are trying to build our
# own return array in the same shape, we need to figure out how the size has changed
# and adapt our stored/returned info accordingly.
length_difference = len(self.model_return_values[pair]) - len_df
i = 0
if length_difference == 0:
i = 1
elif length_difference > 0:
i = length_difference + 1
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
for label in dk.label_list:
df[label].iloc[-1] = predictions[label].iloc[-1]
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
# df['prediction'].iloc[-1] = predictions[-1]
df["do_predict"].iloc[-1] = do_preds[-1]
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
df["DI_values"].iloc[-1] = dk.DI_values[-1]
if length_difference < 0:
prepend_df = pd.DataFrame(
np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
)
df = pd.concat([prepend_df, df], axis=0)
def attach_return_values_to_return_dataframe(self, pair: str, dataframe) -> DataFrame:
"""
Attach the return values to the strat dataframe
:params:
dataframe: DataFrame = strat dataframe
:returns:
dataframe: DataFrame = strat dataframe with return values attached
"""
df = self.model_return_values[pair]
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
dataframe = pd.concat([dataframe[to_keep], df], axis=1)
return dataframe
def return_null_values_to_strategy(self, dataframe: DataFrame, dk) -> None:
"""
Build 0 filled dataframe to return to strategy
"""
dk.find_features(dataframe)
for label in dk.label_list:
dataframe[label] = 0
dataframe[f"{label}_mean"] = 0
dataframe[f"{label}_std"] = 0
# dataframe['prediction'] = 0
dataframe["do_predict"] = 0
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
dataframe["DI_value"] = 0
dk.return_dataframe = dataframe
def purge_old_models(self) -> None:
model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
pattern = re.compile(r"sub-train-(\w+)(\d{10})")
delete_dict: Dict[str, Any] = {}
for dir in model_folders:
result = pattern.match(str(dir.name))
if result is None:
break
coin = result.group(1)
timestamp = result.group(2)
if coin not in delete_dict:
delete_dict[coin] = {}
delete_dict[coin]["num_folders"] = 1
delete_dict[coin]["timestamps"] = {int(timestamp): dir}
else:
delete_dict[coin]["num_folders"] += 1
delete_dict[coin]["timestamps"][int(timestamp)] = dir
for coin in delete_dict:
if delete_dict[coin]["num_folders"] > 2:
sorted_dict = collections.OrderedDict(
sorted(delete_dict[coin]["timestamps"].items())
)
num_delete = len(sorted_dict) - 2
deleted = 0
for k, v in sorted_dict.items():
if deleted >= num_delete:
break
logger.info(f"Freqai purging old model file {v}")
shutil.rmtree(v)
deleted += 1
def update_follower_metadata(self):
# follower needs to load from disk to get any changes made by leader to pair_dict
self.load_drawer_from_disk()
if self.config.get("freqai", {}).get("purge_old_models", False):
self.purge_old_models()
# to be used if we want to send predictions directly to the follower instead of forcing
# follower to load models and inference
# def save_model_return_values_to_disk(self) -> None:
# with open(self.full_path / str('model_return_values.json'), "w") as fp:
# json.dump(self.model_return_values, fp, default=self.np_encoder)
# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
# if exists:
# with open(self.full_path / str('model_return_values.json'), "r") as fp:
# self.model_return_values = json.load(fp)
# elif not self.follow_mode:
# logger.info("Could not find existing datadrawer, starting from scratch")
# else:
# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
# 'sending null values back to strategy')
# return exists, dk

1305
freqtrade/freqai/data_kitchen.py Normal file
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559
freqtrade/freqai/freqai_interface.py Normal file
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@ -0,0 +1,559 @@
# import contextlib
import datetime
import gc
import logging
import shutil
import threading
import time
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.strategy.interface import IStrategy
pd.options.mode.chained_assignment = None
logger = logging.getLogger(__name__)
def threaded(fn):
def wrapper(*args, **kwargs):
threading.Thread(target=fn, args=args, kwargs=kwargs).start()
return wrapper
class IFreqaiModel(ABC):
"""
Class containing all tools for training and prediction in the strategy.
User models should inherit from this class as shown in
templates/ExamplePredictionModel.py where the user overrides
train(), predict(), fit(), and make_labels().
Author: Robert Caulk, rob.caulk@gmail.com
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.assert_config(self.config)
self.freqai_info = config["freqai"]
self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters")
self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters")
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
self.time_last_trained = None
self.current_time = None
self.model = None
self.predictions = None
self.training_on_separate_thread = False
self.retrain = False
self.first = True
self.update_historic_data = 0
self.set_full_path()
self.follow_mode = self.freqai_info.get("follow_mode", False)
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.lock = threading.Lock()
self.follow_mode = self.freqai_info.get("follow_mode", False)
self.identifier = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False
self.ready_to_scan = False
self.first = True
self.keras = self.freqai_info.get("keras", False)
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
def assert_config(self, config: Dict[str, Any]) -> None:
if not config.get("freqai", {}):
raise OperationalException("No freqai parameters found in configuration file.")
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
"""
Entry point to the FreqaiModel from a specific pair, it will train a new model if
necessary before making the prediction.
:params:
:dataframe: Full dataframe coming from strategy - it contains entire
backtesting timerange + additional historical data necessary to train
the model.
:metadata: pair metadata coming from strategy.
"""
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.dd.set_pair_dict_info(metadata)
if self.live:
self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
dk = self.start_live(dataframe, metadata, strategy, self.dk)
# For backtesting, each pair enters and then gets trained for each window along the
# sliding window defined by "train_period" (training window) and "backtest_period"
# (backtest window, i.e. window immediately following the training window).
# FreqAI slides the window and sequentially builds the backtesting results before returning
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dk = self.start_backtesting(dataframe, metadata, self.dk)
dataframe = self.remove_features_from_df(dk.return_dataframe)
return self.return_values(dataframe, dk)
@threaded
def start_scanning(self, strategy: IStrategy) -> None:
"""
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
to improve model youth. This function is agnostic to data preparation/collection/storage,
it simply trains on what ever data is available in the self.dd.
:params:
strategy: IStrategy = The user defined strategy class
"""
while 1:
time.sleep(1)
for pair in self.config.get("exchange", {}).get("pair_whitelist"):
(_, trained_timestamp, _, _) = self.dd.get_pair_dict_info(pair)
if self.dd.pair_dict[pair]["priority"] != 1:
continue
dk = FreqaiDataKitchen(self.config, self.dd, self.live, pair)
# file_exists = False
dk.set_paths(pair, trained_timestamp)
# file_exists = self.model_exists(pair,
# dk,
# trained_timestamp=trained_timestamp,
# model_filename=model_filename,
# scanning=True)
(
retrain,
new_trained_timerange,
data_load_timerange,
) = dk.check_if_new_training_required(trained_timestamp)
dk.set_paths(pair, new_trained_timerange.stopts)
if retrain: # or not file_exists:
self.train_model_in_series(
new_trained_timerange, pair, strategy, dk, data_load_timerange
)
def start_backtesting(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
) -> FreqaiDataKitchen:
"""
The main broad execution for backtesting. For backtesting, each pair enters and then gets
trained for each window along the sliding window defined by "train_period" (training window)
and "backtest_period" (backtest window, i.e. window immediately following the
training window). FreqAI slides the window and sequentially builds the backtesting results
before returning the concatenated results for the full backtesting period back to the
strategy.
:params:
dataframe: DataFrame = strategy passed dataframe
metadata: Dict = pair metadata
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
"""
# Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly
# following tr_train. Both of these windows slide through the
# entire backtest
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
(_, _, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
gc.collect()
dk.data = {} # clean the pair specific data between training window sliding
self.training_timerange = tr_train
# self.training_timerange_timerange = tr_train
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
trained_timestamp = tr_train # TimeRange.parse_timerange(tr_train)
tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
"%Y-%m-%d %H:%M:%S"
)
tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
"%Y-%m-%d %H:%M:%S"
)
logger.info("Training %s", metadata["pair"])
logger.info(f"Training {tr_train_startts_str} to {tr_train_stopts_str}")
dk.data_path = Path(
dk.full_path
/ str(
"sub-train"
+ "-"
+ metadata["pair"].split("/")[0]
+ str(int(trained_timestamp.stopts))
)
)
if not self.model_exists(
metadata["pair"], dk, trained_timestamp=trained_timestamp.stopts
):
self.model = self.train(dataframe_train, metadata["pair"], dk)
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = trained_timestamp.stopts
dk.set_new_model_names(metadata["pair"], trained_timestamp)
dk.save_data(self.model, metadata["pair"], keras_model=self.keras)
else:
self.model = dk.load_data(metadata["pair"], keras_model=self.keras)
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
preds, do_preds = self.predict(dataframe_backtest, dk)
dk.append_predictions(preds, do_preds, len(dataframe_backtest))
print("predictions", len(dk.full_predictions), "do_predict", len(dk.full_do_predict))
dk.fill_predictions(len(dataframe))
return dk
def start_live(
self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dk: FreqaiDataKitchen
) -> FreqaiDataKitchen:
"""
The main broad execution for dry/live. This function will check if a retraining should be
performed, and if so, retrain and reset the model.
:params:
dataframe: DataFrame = strategy passed dataframe
metadata: Dict = pair metadata
strategy: IStrategy = currently employed strategy
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
"""
# update follower
if self.follow_mode:
self.dd.update_follower_metadata()
# get the model metadata associated with the current pair
(_, trained_timestamp, _, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
# if the metadata doesnt exist, the follower returns null arrays to strategy
if self.follow_mode and return_null_array:
logger.info("Returning null array from follower to strategy")
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
# append the historic data once per round
if self.dd.historic_data:
dk.update_historic_data(strategy)
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
# if trainable, check if model needs training, if so compute new timerange,
# then save model and metadata.
# if not trainable, load existing data
if not self.follow_mode:
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
trained_timestamp
)
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
# download candle history if it is not already in memory
if not self.dd.historic_data:
logger.info(
"Downloading all training data for all pairs in whitelist and "
"corr_pairlist, this may take a while if you do not have the "
"data saved"
)
dk.download_all_data_for_training(data_load_timerange)
dk.load_all_pair_histories(data_load_timerange)
if not self.scanning:
self.scanning = True
self.start_scanning(strategy)
elif self.follow_mode:
dk.set_paths(metadata["pair"], trained_timestamp)
logger.info(
"FreqAI instance set to follow_mode, finding existing pair"
f"using { self.identifier }"
)
# load the model and associated data into the data kitchen
self.model = dk.load_data(coin=metadata["pair"], keras_model=self.keras)
if not self.model:
logger.warning("No model ready, returning null values to strategy.")
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dataframe, dk)
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
return dk
def build_strategy_return_arrays(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, pair: str, trained_timestamp: int
) -> None:
# hold the historical predictions in memory so we are sending back
# correct array to strategy
if pair not in self.dd.model_return_values:
pred_df, do_preds = self.predict(dataframe, dk)
self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
return
elif self.dk.check_if_model_expired(trained_timestamp):
pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
do_preds, dk.DI_values = np.ones(2) * 2, np.zeros(2)
logger.warning(
"Model expired, returning null values to strategy. Strategy "
"construction should take care to consider this event with "
"prediction == 0 and do_predict == 2"
)
else:
# Only feed in the most recent candle for prediction in live scenario
pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
return
def check_if_feature_list_matches_strategy(
self, dataframe: DataFrame, dk: FreqaiDataKitchen
) -> None:
"""
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
to a folder holding existing models.
:params:
dataframe: DataFrame = strategy provided dataframe
dk: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop
"""
dk.find_features(dataframe)
if "training_features_list_raw" in dk.data:
feature_list = dk.data["training_features_list_raw"]
else:
feature_list = dk.training_features_list
if dk.training_features_list != feature_list:
raise OperationalException(
"Trying to access pretrained model with `identifier` "
"but found different features furnished by current strategy."
"Change `identifer` to train from scratch, or ensure the"
"strategy is furnishing the same features as the pretrained"
"model"
)
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
"""
Base data cleaning method for train
Any function inside this method should drop training data points from the filtered_dataframe
based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"):
dk.principal_component_analysis()
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers"):
dk.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold"):
dk.data["avg_mean_dist"] = dk.compute_distances()
# if self.feature_parameters["determine_statistical_distributions"]:
# dk.determine_statistical_distributions()
# if self.feature_parameters["remove_outliers"]:
# dk.remove_outliers(predict=False)
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
"""
Base data cleaning method for predict.
These functions each modify dk.do_predict, which is a dataframe with equal length
to the number of candles coming from and returning to the strategy. Inside do_predict,
1 allows prediction and < 0 signals to the strategy that the model is not confident in
the prediction.
See FreqaiDataKitchen::remove_outliers() for an example
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"):
dk.pca_transform(dataframe)
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers"):
dk.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold"):
dk.check_if_pred_in_training_spaces()
# if self.feature_parameters["determine_statistical_distributions"]:
# dk.determine_statistical_distributions()
# if self.feature_parameters["remove_outliers"]:
# dk.remove_outliers(predict=True) # creates dropped index
def model_exists(
self,
pair: str,
dk: FreqaiDataKitchen,
trained_timestamp: int = None,
model_filename: str = "",
scanning: bool = False,
) -> bool:
"""
Given a pair and path, check if a model already exists
:param pair: pair e.g. BTC/USD
:param path: path to model
"""
coin, _ = pair.split("/")
if not self.live:
dk.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
path_to_modelfile = Path(dk.data_path / str(model_filename + "_model.joblib"))
file_exists = path_to_modelfile.is_file()
if file_exists and not scanning:
logger.info("Found model at %s", dk.data_path / dk.model_filename)
elif not scanning:
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
return file_exists
def set_full_path(self) -> None:
self.full_path = Path(
self.config["user_data_dir"] / "models" / str(self.freqai_info.get("identifier"))
)
self.full_path.mkdir(parents=True, exist_ok=True)
shutil.copy(
self.config["config_files"][0],
Path(self.full_path, Path(self.config["config_files"][0]).name),
)
def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
"""
Remove the features from the dataframe before returning it to strategy. This keeps it
compact for Frequi purposes.
"""
to_keep = [
col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%")
]
return dataframe[to_keep]
def train_model_in_series(
self,
new_trained_timerange: TimeRange,
pair: str,
strategy: IStrategy,
dk: FreqaiDataKitchen,
data_load_timerange: TimeRange,
):
"""
Retreive data and train model in single threaded mode (only used if model directory is empty
upon startup for dry/live )
:params:
new_trained_timerange: TimeRange = the timerange to train the model on
metadata: dict = strategy provided metadata
strategy: IStrategy = user defined strategy object
dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators
(larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs)
"""
corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes(
data_load_timerange, pair
)
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
strategy, corr_dataframes, base_dataframes, pair
)
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
# find the features indicated by strategy and store in datakitchen
dk.find_features(unfiltered_dataframe)
model = self.train(unfiltered_dataframe, pair, dk)
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
dk.set_new_model_names(pair, new_trained_timerange)
self.dd.pair_dict[pair]["first"] = False
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
with self.lock:
self.dd.pair_to_end_of_training_queue(pair)
dk.save_data(model, coin=pair, keras_model=self.keras)
if self.freqai_info.get("purge_old_models", False):
self.dd.purge_old_models()
# self.retrain = False
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
@abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
@abstractmethod
def fit(self) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:params:
data_dictionary: Dict = the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
return
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
) -> Tuple[DataFrame, npt.ArrayLike]:
"""
Filter the prediction features data and predict with it.
:param:
unfiltered_dataframe: Full dataframe for the current backtest period.
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:return:
:predictions: np.array of predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
"""
def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User defines the labels here (target values).
:params:
dataframe: DataFrame = the full dataframe for the present training period
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
"""
return
@abstractmethod
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User defines the dataframe to be returned to strategy here.
:params:
dataframe: DataFrame = the full dataframe for the current prediction (live)
or --timerange (backtesting)
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:returns:
dataframe: DataFrame = dataframe filled with user defined data
"""
return

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freqtrade/freqai/prediction_models/CatboostPredictionModel.py Normal file
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import logging
from typing import Any, Dict, Tuple
from catboost import CatBoostRegressor, Pool
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
logger = logging.getLogger(__name__)
class CatboostPredictionModel(IFreqaiModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User uses this function to add any additional return values to the dataframe.
e.g.
dataframe['volatility'] = dk.volatility_values
"""
return dataframe
def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User defines the labels here (target values).
:params:
:dataframe: the full dataframe for the present training period
"""
dataframe["s"] = (
dataframe["close"]
.shift(-self.feature_parameters["period"])
.rolling(self.feature_parameters["period"])
.mean()
/ dataframe["close"]
- 1
)
return dataframe["s"]
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("--------------------Starting training " f"{pair} --------------------")
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
# optional additional data cleaning/analysis
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
logger.info(f"--------------------done training {pair}--------------------")
return model
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
train_data = Pool(
data=data_dictionary["train_features"],
label=data_dictionary["train_labels"],
weight=data_dictionary["train_weights"],
)
test_data = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"],
weight=data_dictionary["test_weights"],
)
model = CatBoostRegressor(
allow_writing_files=False,
verbose=100,
early_stopping_rounds=400,
**self.model_training_parameters,
)
model.fit(X=train_data, eval_set=test_data)
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)
for label in dk.label_list:
pred_df[label] = (
(pred_df[label] + 1)
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
/ 2
) + dk.data["labels_min"][label]
return (pred_df, dk.do_predict)

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freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py Normal file
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import logging
from typing import Any, Dict, Tuple
from catboost import CatBoostRegressor # , Pool
from pandas import DataFrame
from sklearn.multioutput import MultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
logger = logging.getLogger(__name__)
class CatboostPredictionMultiModel(IFreqaiModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User uses this function to add any additional return values to the dataframe.
e.g.
dataframe['volatility'] = dk.volatility_values
"""
return dataframe
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("--------------------Starting training " f"{pair} --------------------")
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
# optional additional data cleaning/analysis
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
logger.info(f"--------------------done training {pair}--------------------")
return model
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
cbr = CatBoostRegressor(
allow_writing_files=False,
gpu_ram_part=0.5,
verbose=100,
early_stopping_rounds=400,
**self.model_training_parameters,
)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
# eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
model = MultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)
for label in dk.label_list:
pred_df[label] = (
(pred_df[label] + 1)
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
/ 2
) + dk.data["labels_min"][label]
return (pred_df, dk.do_predict)

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freqtrade/freqai/prediction_models/LightGBMPredictionModel.py Normal file
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import logging
from typing import Any, Dict, Tuple
from lightgbm import LGBMRegressor
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
logger = logging.getLogger(__name__)
class LightGBMPredictionModel(IFreqaiModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User uses this function to add any additional return values to the dataframe.
e.g.
dataframe['volatility'] = dk.volatility_values
"""
return dataframe
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("--------------------Starting training " f"{pair} --------------------")
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
# optional additional data cleaning/analysis
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
logger.info(f"--------------------done training {pair}--------------------")
return model
def fit(self, data_dictionary: Dict) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
model = LGBMRegressor(seed=42, n_estimators=2000, verbosity=1, force_col_wise=True)
model.fit(X=X, y=y, eval_set=eval_set)
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:predictions: np.array of predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
# logger.info("--------------------Starting prediction--------------------")
original_feature_list = dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, original_feature_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)
for label in dk.label_list:
pred_df[label] = (
(pred_df[label] + 1)
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
/ 2
) + dk.data["labels_min"][label]
return (pred_df, dk.do_predict)

12
freqtrade/freqai/strategy_bridge.py Normal file
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@ -0,0 +1,12 @@
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
class CustomModel:
"""
A bridge between the user defined IFreqaiModel class
and the strategy.
"""
def __init__(self, config):
self.bridge = FreqaiModelResolver.load_freqaimodel(config)

5
freqtrade/optimize/backtesting.py
View File

@ -206,6 +206,11 @@ class Backtesting:
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
if self.config.get('freqai') is not None:
self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 1000))
logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
self.config['startup_candle_count'] = self.required_startup
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,

11
freqtrade/plugins/pairlist/pairlist_helpers.py
View File

@ -40,3 +40,14 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
except re.error as err:
raise ValueError(f"Wildcard error in {pair_wc}, {err}")
return result
def dynamic_expand_pairlist(config: dict, markets: list) -> List[str]:
if config.get('freqai', {}):
full_pairs = config['pairs'] + [pair for pair in config['freqai']['corr_pairlist']
if pair not in config['pairs']]
expanded_pairs = expand_pairlist(full_pairs, markets)
else:
expanded_pairs = expand_pairlist(config['pairs'], markets)
return expanded_pairs

6
freqtrade/resolvers/exchange_resolver.py
View File

@ -18,7 +18,8 @@ class ExchangeResolver(IResolver):
object_type = Exchange
@staticmethod
def load_exchange(exchange_name: str, config: dict, validate: bool = True) -> Exchange:
def load_exchange(exchange_name: str, config: dict, validate: bool = True,
freqai: bool = False) -> Exchange:
"""
Load the custom class from config parameter
:param exchange_name: name of the Exchange to load
@ -31,7 +32,8 @@ class ExchangeResolver(IResolver):
try:
exchange = ExchangeResolver._load_exchange(exchange_name,
kwargs={'config': config,
'validate': validate})
'validate': validate,
'freqai': freqai})
except ImportError:
logger.info(
f"No {exchange_name} specific subclass found. Using the generic class instead.")

50
freqtrade/resolvers/freqaimodel_resolver.py Normal file
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@ -0,0 +1,50 @@
# pragma pylint: disable=attribute-defined-outside-init
"""
This module load a custom model for freqai
"""
import logging
from pathlib import Path
from typing import Dict
from freqtrade.constants import USERPATH_FREQAIMODELS
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.freqai_interface import IFreqaiModel
from freqtrade.resolvers import IResolver
logger = logging.getLogger(__name__)
class FreqaiModelResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt loss class
"""
object_type = IFreqaiModel
object_type_str = "FreqaiModel"
user_subdir = USERPATH_FREQAIMODELS
initial_search_path = Path(__file__).parent.parent.joinpath(
"freqai/prediction_models").resolve()
@staticmethod
def load_freqaimodel(config: Dict) -> IFreqaiModel:
"""
Load the custom class from config parameter
:param config: configuration dictionary
"""
freqaimodel_name = config.get("freqaimodel")
if not freqaimodel_name:
raise OperationalException(
"No freqaimodel set. Please use `--freqaimodel` to "
"specify the FreqaiModel class to use.\n"
)
freqaimodel = FreqaiModelResolver.load_object(
freqaimodel_name,
config,
kwargs={"config": config},
extra_dir=config.get("freqaimodel_path"),
)
return freqaimodel

17
freqtrade/strategy/interface.py
View File

@ -546,6 +546,23 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return None
def populate_any_indicators(self, basepair: str, pair: str, df: DataFrame, tf: str,
informative: DataFrame = None, coin: str = "",
set_generalized_indicators: bool = False) -> DataFrame:
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User can add
additional features here, but must follow the naming convention.
Defined in IStrategy because Freqai needs to know it exists.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
return df
###
# END - Intended to be overridden by strategy
###

342
freqtrade/templates/FreqaiExampleStrategy.py Normal file
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@ -0,0 +1,342 @@
import logging
from functools import reduce
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
class FreqaiExampleStrategy(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.model = CustomModel(self.config)
self.model.bridge.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
canonical freqtrade configuration file under config['freqai'].
"""
minimal_roi = {"0": 0.1, "240": -1}
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": {
"do_predict": {"color": "brown"},
},
},
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
with self.model.bridge.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
informative[f"{coin}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"{coin}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{coin}close_over_20sma-period_{t}"] = (
informative["close"] / informative[f"{coin}20sma-period_{t}"]
)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
)
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
macd = ta.MACD(informative, timeperiod=t)
informative[f"%-{coin}macd-period_{t}"] = macd["macd"]
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["period"])
.rolling(self.freqai_info["feature_parameters"]["period"])
.mean()
/ df["close"]
- 1
)
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
self.pair = metadata["pair"]
sgi = True
# the following loops are necessary for building the features
# indicated by the user in the configuration file.
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
for tf in self.freqai_info["timeframes"]:
dataframe = self.populate_any_indicators(
metadata,
self.pair,
dataframe.copy(),
tf,
coin=self.pair.split("/")[0] + "-",
set_generalized_indicators=sgi,
)
sgi = False
for pair in self.freqai_info["corr_pairlist"]:
if metadata["pair"] in pair:
continue # do not include whitelisted pair twice if it is in corr_pairlist
dataframe = self.populate_any_indicators(
metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
)
# the model will return 4 values, its prediction, an indication of whether or not the
# prediction should be accepted, the target mean/std values from the labels used during
# each training period.
dataframe = self.model.bridge.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
if enter_short_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df
def get_ticker_indicator(self):
return int(self.config["timeframe"][:-1])
def custom_exit(
self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
):
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
if trade_candle.empty:
return None
trade_candle = trade_candle.squeeze()
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.model.bridge.dd.pair_dict
else:
pair_dict = self.model.bridge.dd.follower_dict
entry_tag = trade.enter_tag
if (
"prediction" + entry_tag not in pair_dict[pair]
or pair_dict[pair]["prediction" + entry_tag] > 0
):
with self.model.bridge.lock:
pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
roi_price = pair_dict[pair]["prediction" + entry_tag]
roi_time = self.max_roi_time_long.value
roi_decay = roi_price * (
1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
)
if roi_decay < 0:
roi_decay = self.linear_roi_offset.value
else:
roi_decay += self.linear_roi_offset.value
if current_profit > roi_decay:
return "roi_custom_win"
if current_profit < -roi_decay:
return "roi_custom_loss"
def confirm_trade_exit(
self,
pair: str,
trade: Trade,
order_type: str,
amount: float,
rate: float,
time_in_force: str,
exit_reason: str,
current_time,
**kwargs,
) -> bool:
entry_tag = trade.enter_tag
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.model.bridge.dd.pair_dict
else:
pair_dict = self.model.bridge.dd.follower_dict
with self.model.bridge.lock:
pair_dict[pair]["prediction" + entry_tag] = 0
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
return True
def confirm_trade_entry(
self,
pair: str,
order_type: str,
amount: float,
rate: float,
time_in_force: str,
current_time,
entry_tag,
side: str,
**kwargs,
) -> bool:
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze()
if side == "long":
if rate > (last_candle["close"] * (1 + 0.0025)):
return False
else:
if rate < (last_candle["close"] * (1 - 0.0025)):
return False
return True

1
mkdocs.yml
View File

@ -35,6 +35,7 @@ nav:
- Edge Positioning: edge.md
- Advanced Strategy: strategy-advanced.md
- Advanced Hyperopt: advanced-hyperopt.md
- Freqai: freqai.md
- Sandbox Testing: sandbox-testing.md
- FAQ: faq.md
- SQL Cheat-sheet: sql_cheatsheet.md

9
requirements-freqai.txt Normal file
View File

@ -0,0 +1,9 @@
# Include all requirements to run the bot.
-r requirements.txt
# Required for freqai
scikit-learn==1.0.2
scikit-optimize==0.9.0
joblib==1.1.0
catboost==1.0.4
lightgbm==3.3.2

0
user_data/freqaimodels/.gitkeep Normal file
View File